In conventional computing, a single computer system is often utilized to perform operations on data. The operations may be performed by a single processor, or central processing unit (CPU) within the computer. The operations performed on the data may include numerical calculations, or database access, for example. The CPU may perform the operations under the control of a stored program containing executable code. The code may include a series of instructions that may be executed by the CPU that cause the computer to perform the operations on the data. The capability of a computer in performing these operations may be measured in units of millions of instructions per second (MIPS), or millions of operations per second (MOPS).
Historically, increases in computer performance have depended on improvements in integrated circuit technology, and were often governed by the principles of “Moore's law”. Moore's law postulates that the speed of integrated circuit devices may increase at a predictable, and approximately constant, rate over time. However, technology limitations may begin to limit the ability to maintain predictable speed improvements in integrated circuit devices.
Another approach to increasing computer performance implements changes in computer architecture. For example, the introduction of parallel processing may be utilized. In a parallel processing approach, computer systems may utilize a plurality of CPUs within a computer system that may work together to perform operations on data. Parallel processing computers may offer computing performance that may increase as the number of parallel processing CPUs in increased. The size and expense of parallel processing computer systems result in special purpose computer systems. This may limit the range of applications in which the systems may be feasibly or economically utilized.
An alternative to large parallel processing computer systems is cluster computing. In cluster computing, a plurality of smaller computer, connected via a network, may work together to perform operations on data. Cluster computing systems may be implemented, for example, utilizing relatively low cost, general purpose, personal computers or servers. In a cluster computing environment, computers in the cluster may exchange information across a network similar to the way that parallel processing CPUs exchange information across an internal bus. Cluster computing systems may also scale to include networked supercomputers. The collaborative arrangement of computers working cooperatively to perform operations on data may be referred to as high performance computing (HPC).
Cluster computing offers the promise of systems with greatly increased computing performance relative to single processor computers by enabling a plurality of processors distributed across a network to work cooperatively to solve computationally intensive computing problems. One aspect of cooperation between computers may include the sharing of information among computers. Remote direct memory access (RDMA) is a method that enables a processor in a local computer to gain direct access to memory in a remote computer across the network. RDMA may provide improved information transfer performance when compared to traditional communications protocols. RDMA has been deployed in local area network (LAN) environments some of which have been standardized and others which are proprietary. RDMA, when utilized in wide area network (WAN) and Internet environments, is referred to as RDMA over TCP, RDMA over IP, or RDMA over TCP/IP.
In a system utilizing storage area network (SAN) technology, data from a file may be distributed among a plurality of physical and/or logical storage devices, for example. A portion of the file, which is stored in a physical or logical storage device, may be referred to as a “data stripe”. A client device, which may be communicatively coupled to an SAN device, may store files and/or data in, or retrieve files and/or data from, the SAN device. An exemplary client device may comprise a personal computer, or workstation. The SAN device may comprise a server device, which may receive files and/or data from the client device, segment the received files and/or data into data stripes, and store each data stripe in at least one of the physical and/or logical storage devices within the SAN device. The server device may also retrieve files and/or data in response to a request from the client device. In this aspect, the server device may retrieve a plurality of data stripes, which may be assembled to reconstitute the retrieved file.
In some conventional SAN systems, the server may retrieve and store each of the retrieved data stripes until the retrieved file has been reconstituted. The reconstituted file may then be transmitted, by the server, to the requesting client device, via a network. One disadvantage in this approach is that the server may be required to allocate physical storage resources, for example memory, which may be utilized to store data stripes until a sufficient number of data stripes have been retrieved to reconstitute the retrieved file.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of such systems with some aspects of the present invention as set forth in the remainder of the present application with reference to the drawings.